Apparatus and software system for and method of performing a visual-relevance-rank subsequent search

ABSTRACT

A method analyzes the visual content of media such as videos for collecting together visually-similar appearances in their constituent images (e.g. same scenes, same objects, faces of the same people.) As a result, the most relevant and salient (of clearest and largest presence) visual appearances depicted in the videos are presented to the user, both for the sake of summarizing the video content for the users to “see before they watch” (that is, judge by the depicted video content in a filmstrip-like summary whether they want to mouse-click on the video and actually spend time watching it), as well as for allowing to users to further refine their video search result set according to the most relevant and salient video content returned (e.g. largest screen-time faces).

CLAIM OF PRIORITY

This application is a continuation application of U.S. patentapplication Ser. No. 12/502,202, entitled “APPARATUS AND SOFTWARE SYSTEMFOR AND METHOD OF PERFORMING A VISUAL-RELEVANCE-RANK SUBSEQUENT SEARCH,”filed Jul. 13, 2009, which claims priority to U.S. ProvisionalApplication No. 61/079,845 filed Jul. 11, 2008 and is related to priorU.S. Pat. No. 7,920,748 issued Apr. 5, 2011, U.S. Pat. No. 7,903,899issued Mar. 8, 2011, U.S. Pat. No. 8,059,915 issued Nov. 15, 2011, eachof which are specifically and fully incorporated herein by reference inits entirety.

FIELD OF THE INVENTION

The invention is directed to searching content including video andmultimedia and, more particularly, to searching video content andpresenting candidate results based on relevance and suggestingsubsequent narrowing and additional searches based on rankings of priorsearch results.

BACKGROUND

The prior art includes various searching methods and systems directed toidentifying and retrieving content based on key words found in the filename, tags on associated web pages, transcripts, text of hyperlinkspointing to the content, etc. Such search methods rely on Booleanoperators indicative of the presence or absence of search terms.However, a more robust search method is required to identify contentsatisfying search requirements and to enhance searching techniquesrelated to video and multimedia content and objects.

SUMMARY OF THE INVENTION

The invention is directed to a robust search method providing forenhanced searching of content taking into consideration not only theexistence (or absence) of certain characteristics (as might be indicatedby corresponding “tags” attached to the content or portions thereof,e.g., files), but the importance of those characteristics with respectto the content. Tags may name or describe a feature, quality of, and/orobjects associated with the content (e.g., video file) and/or of objectsappearing in the content (e.g., an object appearing within a video fileand/or associated with one or more objects appearing in a video fileand/or associated with objects appearing in the video file.)

Search results, whether or not based on search criteria specifyingimportance values, may include importance values for the tags that weresearched for and identified within the content. Additional tags (e.g.,tags not part of the preceding queried search terms) may also beprovided and displayed to the user including, for example, tags forother characteristics suggested by the preceding search and/or suggestedtags that might be useful as part of a subsequent search. Suggested tagsmay be based in part on past search histories, user profile information,etc. and/or may be directed to related products and/or servicessuggested by the prior search or search results.

Results of searches may further include a display of “thumbnails”corresponding and linking to content most closely satisfying searchcriteria, the thumbnails arranged in order of match quality with thesize of the thumbnail indicative of its match quality (e.g., bestmatching video files indicated by large thumbnail images, next best byintermediate size thumbnails, etc.) As used herein, the term “thumbnail”includes a frame representing a scene, typically the frame image itselfextracted from the set of frames constituting the scene or “shot”.However, a thumbnail may be a static image extracted from a portion of aframe from the scene, an image generated to otherwise correspond to theimagery content of the scene, or a dynamic image including motion, aninteractive image providing additional viewing and user functionalityincluding zooming, display of adjacent frames of the scene (e.g., afilmstrip of sub-scenes or adjacent frames), etc. A user may click onand/or hover over a thumbnail to enlarge the thumbnail, be presentedwith a preview of the content (e.g., a video clip most relevant to thesearch terms and criteria) and/or to retrieve or otherwise access thecontent.

Often the format of the search results, e.g. thumbnails, does notreadily provide a satisfactory reorientation of the identified object,e.g., the content of an entire video typically including several scenes.Further, the display of search results may not be tightly integrated, ifat all, with an appropriate user interface that may not readily assistthe user to narrow, redirect and/or redefine a search without requiringcreation of a new query expression.

Note, as used herein, the term “scene” may include a sequence of framesin which there is some commonality of objects appearing in the framesincluding either or both foreground and background objects. A scene maycomprise contiguous or discontinuous sequences of frames of a video.

Embodiments of the present invention include apparatus, software andmethods that analyze the visual content of media such as videos forcollecting together visually-similar appearances in their constituentimages (e.g. same scenes, same objects, faces of the same people.) As aresult, the most relevant and salient (of clearest and largest presence)visual appearances depicted in the videos are presented to the user,both for the sake of summarizing the video content for the users to “seebefore they watch” (that is, judge by the depicted video content in afilmstrip-like summary whether they want to mouse-click on the video andactually spend time watching it), as well as for allowing to users tofurther refine their video search result set according to the mostrelevant and salient video content returned (e.g. largest screen-timefaces).

While the following description of a preferred embodiment of theinvention uses an example based on indexing and searching of videocontent, e.g., video files, visual objects, etc., embodiments of theinvention are equally applicable to processing, organizing, storing andsearching a wide range of content types including video, audio, text andsignal files. Thus, an audio embodiment may be used to provide asearchable database of and search audio files for speech, music, orother audio types for desired characteristics of specified importance.Likewise, embodiments may be directed to content in the form of orrepresented by text, signals, etc.

It is further noted that the use of the term “engine” in describingembodiments and features of the invention is not intended to be limitingof any particular implementation for accomplishing and/or performing theactions, steps, processes, etc. attributable to and/or performed by theengine. An engine may be, but is not limited to, software, hardwareand/or firmware or any combination thereof that performs the specifiedfunctions including, but not limited to, any using a general and/orspecialized processor in combination with appropriate software. Softwaremay be stored in or using a suitable machine-readable medium such as,but not limited to, random access memory (RAM) and other forms ofelectronic storage, data storage media such as hard drives, removablemedia such as CDs and DVDs, etc. Further, any name associated with aparticular engine is, unless otherwise specified, for purposes ofconvenience of reference and not intended to be limiting to a specificimplementation. Additionally, any functionality attributed to an enginemay be equally performed by multiple engines, incorporated into and/orcombined with the functionality of another or different engine, ordistributed across one or more engines of various configurations.

It is further noted that the following summary of the invention includesvarious examples to provide the reader with a context and/orembodiment(s) and thereby assist the reader's understanding andappreciation for and of the related technology. However, unlessotherwise stated or evident from context, the examples are by way ofillustration only and are not intended or to be considered limiting ofthe various aspects and features of the invention.

According to an aspect of the invention, a method comprises the steps ofreceiving a search string; searching for videos satisfying searchcriteria based on the search string; identifying visual objects in thevideos; grouping the videos based on the visual objects; displayingimages of the visual objects in association with respective ones of thegroups of videos; selecting one of the groups of videos; and displayinga result of the searching step in an order responsive to the selectingstep. For example, in response to a search initiate either by entry of atext-based query or a graphically-based search request (e.g., search forimages similar to that clicked-on), resultant videos are grouped basedon image content, e.g., according to a featured person or object in thevideo.

According to another aspect of the invention, a method of identifying avideo comprises the steps of identifying sequences of frames of thevideo as comprising respective scenes; determining a visual relevancerank of each of the scenes; selecting a number of the scenes based onthe visual relevance rank associated with each of the scenes;identifying, within each of the selected scenes, a representativethumbnail frame; and displaying (i) a first thumbnail corresponding toone of the representative thumbnail frames based on the visual relevancerank of the associated scene and (ii) a filmstrip including an orderedsequence of the representative thumbnail frames.

According to a feature of the invention, the “thumbnails” may includeone or more frames (e.g., images) of the corresponding scene of thevideo, the frame representing (e.g., visually depicting) the scene,typically having been extracted directly from the video. According toother features of the invention, a thumbnail may be a static imageextracted from a portion of the frame, an image generated to otherwisecorrespond to the imagery content of the scene, a dynamic imageincluding motion, and/or an interactive image providing additionalviewing and user functionality including zooming, display of adjacentframes of the scene (e.g., a filmstrip of sub-scenes), etc. A user mayclick on and/or hover over a thumbnail to enlarge the thumbnail, bepresented with a preview of the content (e.g., a video clip mostrelevant to the search terms and criteria) and/or to retrieve orotherwise access the content.

According to a feature of the invention, the method may include linkingeach of the thumbnails to a corresponding one of the scenes. Accordingto an aspect of the invention, linking may be accomplished by providinga clickable hyperlink to the video and to a location in the videocorresponding to start or other portion of the scene so that clicking onthe link may initiate playing the video at the selected scene and/orspecific frame within the scene. Thus, according to another feature ofthe invention, the method may include recognizing a selection of one ofthe thumbnails and playing the video starting at the scene correspondingto the selected thumbnail.

According to another feature of the invention, the visual relevance rankmay be based on a visual importance of the associated scene. Forexample, certain frames may be more visually informative and/orimportant to a user about the content of the scene including framesdepicting people and faces, frames including an object determined to beimportant to the scene based on object placement, lighting, size, etc.In contrast, frames having certain characteristic may be lessinformative, interesting and/or important to a user in making aselection including frames having low contrast, little or no detectedmotion of a central object or no central object, frames havingsignificant amounts of text, etc. These less interesting frames may beranked lower than the more interesting frames and/or have their rankingdecreased.

According to another feature of the invention, the visual relevance rankmay be based on a contextual importance of the associated scene. Forexample, the frame may include an object that satisfies search criteriathat resulted in identification and/or selection of the video such as aface or person in the video that was the subject of the search or otherreason why the video was identified. Frames containing the target objectmay be ranked more highly and thereby selected for display over otherframes.

According to another feature of the invention, the step of identifyingmay include designating a type of object to be included in each of therepresentative thumbnail frames. For example, a user may select “facesonly” so that only frames depicting human faces are displayed while thedisplay of other frames may be suppressed. The type of object may beselected and include, for example, faces, people, cars, and movingobjects.

According to another feature of the invention, the visual relevance rankof a scene may be downgraded for those scenes having specifiedcharacteristics. For example, frames types determined to be less likelyto provide useful information to a user in determining the content of avideo, video scene, clip, etc. may be suppressed. Low interest framesmay include frames with low contract, lacking identifiable human faces(sometimes referenced herein as “no faces”), or other visual indiciadiscernable from the content of the frame and/or those frames notclearly including a targeted search object may be ranked lower and/ortheir ranking decreased to suppress display of those frames as part of afilmstrip presentation of the video.

According to another feature of the invention, the specifiedcharacteristics may be selected from the group consisting of sceneshaving low contrast images, scenes having a significant textual contentand scenes having relatively little or no or little foreground objectmotion.

According to another feature of the invention, the step of identifyingsequences of frames of the video as comprising respective scenes mayinclude identifying one or more regions of interest appearing in theframes and segmenting sequences of the frames into scenes based oncontinuity of objects appearing in frames of the sequences of frames.For example, a scene may be defined as those frames including images ofa certain set of objects such as faces.

According to another feature of the invention, the step of identifyingsequences of frames of the video as comprising respective scenes mayinclude identifying objects appearing in the frames and segmentingsequences of the frames into scenes based on continuity of objectsappearing in frames of the sequences of frames.

According to another feature of the invention, the step of identifyingsequences of frames of the video as comprising respective scenes mayinclude identifying an object appearing in the frames and segmentingsequences of the frames into scenes based the object appearing in framesof the sequences of frames.

According to another feature of the invention, the frames of thesequence of frames may be discontinuous. For example, a scene may bedefined as individual but noncontiguous clips (sequences of frames) inwhich a particular set of objects appear, such as a person, even thoughthe images of that person are interrupted by intervening shots orscenes.

According to another feature of the invention, the step of determiningvisual relevance rank of each of the scenes may be determined byidentifying an object appearing in the scene that correspond to a targetobject specified by search criteria. According to another feature of theinvention, the target object may be a person and/or face wherein theidentity of the person is discernable and, more preferable,identifiable.

According to another feature of the invention, the step of determiningthe visual relevance rank of each of the scenes may include identifyinga tube length corresponding to a duration of each of the scenes and, inresponse, calculating a visual relevance rank score for each of thescenes. According to an aspect of the invention, “tubes” may be datastructures or other means of representing time-space threads that track,can be used to track, or define an object or objects across multipleframes.

According to another feature of the invention, the step of identifying,within each of the selected scenes, a representative thumbnail frame mayinclude reviewing frames of each of the selected scenes and selecting aframe as a representative thumbnail including an object visual relevancerank to an associated scene.

According to another feature of the invention, the step of selecting aframe as a representative thumbnail may include identifying an objectappearing in a scene that corresponds to a target object specified bysearch criteria.

According to another feature of the invention, the step of displaying afilmstrip may include ordering the representative thumbnail frames in atemporal sequence corresponding to an order of appearance of theselected scenes in the video. For example, once the thumbnail or framesare ranked to identify the most salient or important frames, the framesare put back into a sequence corresponding to their order of appearancein the video, e.g., back into the original temporal order.

According to another feature of the invention, selection of a thumbnailis detected or identified resulting in the associated video beingplayed. For example, a user may “click on” a thumbnail to play thecorresponding video or scene of the video, hover over a thumbnail toplay a portion of the video (e.g., the depicted scene), etc.

According to another aspect of the invention, a method of selecting avideo to be played includes searching a database of videos to identifyvideos satisfying some search criteria; displaying links to the videossatisfying the search criteria according to one or more of the methodsdescribed above; identifying a selection of a thumbnail associated withone of the identified videos to identify a selected video; and playingthe selected video.

According to another aspect of the invention, a method comprises thesteps of receiving a search query specifying search criteria; searchingfor videos satisfying the search criteria; identifying visual objects inthe videos; grouping the videos based on the visual objects; anddisplaying images of the visual objects in association with respectiveones of the groups of videos. For example, videos identified orretrieved by the search may be grouped or sorted based on visual contentsuch as who appears in each video.

According to a feature of the invention, the method may includeselecting one of the groups of videos and displaying a result of thesearching step in an order responsive to the selecting step. Forexample, if a selected video includes images of a particular identifiedperson, other videos including that person may be pushed up the listorder and displayed earlier in a revised listing of search results.

According to another feature of the invention, the objects may compriseimages of faces.

According to another feature of the invention, the step of identifyingmay include determining which of the objects appear most frequently inthe videos. For example, if a particular person appears prominentlythroughout a scene, the scene may be tightly associated with thatpersons while people and/or objects having a lesser presence in thescene may be less tightly associated.

According to another feature of the invention, the step of identifyingmay include determining a ranking of the objects. For example, videoshaving prominently featured and/or identifiable people may be preferredover videos where image content is less well defined.

According to another feature of the invention, the step of ranking theobjects may be based on prominency of appearance and duration of each ofthe objects within the videos.

According to another feature of the invention, the method may furtherinclude ranking the objects and increasing a ranking of videoscontaining the ranked objects based on the rankings of the objects.

According to another feature of the invention, the method may furtherinclude displaying links to the videos in an order determined by therankings of the videos.

According to another aspect of the invention, an apparatus foridentifying a video comprises a scene detection engine operating toidentify sequences of frames of the video as comprising respectivescenes; a scoring engine operating to determine a visual relevance rankof each of the scenes; a scene selection engine operating to select anumber of the scenes based on the visual relevance rank associated witheach of the scenes; a frame extraction engine operating to identify,within each of the selected scenes, and a representative thumbnailframe; a display engine operating to generate a visual display including(i) a first thumbnail corresponding to one of the representativethumbnail frames based on the visual relevance rank of the associatedscene and (ii) a filmstrip including an ordered sequence of therepresentative thumbnail frames.

According to another aspect of the invention, video browser forsearching videos includes a search engine operating to search a databaseof videos and identify videos satisfying some search criteria; anapparatus for displaying links to the videos satisfying the searchcriteria as described above; an interface operating to identify aselection of a thumbnail associated with one of the identified videos toidentify a selected video; and a video player operating to play theselected video.

According to another aspect of the invention, a computer programcomprises a computer usable medium (e.g., memory device and/or medium)having computer readable program code embodied therein, the computerreadable program code including computer readable program code forcausing the computer to identify sequences of frames of the video ascomprising respective scenes; computer readable program code for causingthe computer to determine a visual relevance rank of each of the scenes;computer readable program code for causing the computer to select anumber of the scenes based on the visual relevance rank associated witheach of the scenes; computer readable program code for causing thecomputer to identify, within each of the selected scenes, arepresentative thumbnail frame; and computer readable program code forcausing the computer to display (i) a first thumbnail corresponding toone of the representative thumbnail frames based on the visual relevancerank of the associated scene and (ii) a filmstrip including an orderedsequence of the representative thumbnail frames.

According to another aspect of the invention, a computer programcomprises a computer usable medium having computer readable program codeembodied therein, the computer readable program code including computerreadable program code for causing the computer to receive a search queryspecifying search criteria; computer readable program code for causingthe computer to search for videos satisfying the search criteria;computer readable program code for causing the computer to identifyvisual objects in the videos; computer readable program code for causingthe computer to group the videos based on the visual objects; andcomputer readable program code for causing the computer to displayimages of the visual objects in association with respective ones of thegroups of videos.

Additional objects, advantages and novel features of the invention willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing and the accompanying drawings or may be learned by practice ofthe invention. The objects and advantages of the invention may berealized and attained by means of the instrumentalities and combinationsparticularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawing figures depict preferred embodiments of the presentinvention by way of example, not by way of limitations. In the figures,like reference numerals refer to the same or similar elements.

FIG. 1 is a flow chart of a method of extracting frames from videossatisfying some search criteria for display to a user;

FIG. 2 is a flow chart of a method searching for and retrieving contentbased on visual relevance rank of objects identified searched videos;

FIG. 3 is a screen shot of a user interface used to enter searchcriteria for identifying a target video, display results in a rankedorder, and provide for interactive refinement of search criteria;

FIG. 4 is another screen shot of the user interface of FIG. 3.

FIG. 5 is another screen shot of the user interface of FIG. 3; and

FIG. 6 is a block diagram of a computer platform for executing computerprogram code implementing processes and steps according to variousembodiments of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

While the following preferred embodiment of the invention uses anexample based on indexing and searching of video content, e.g., videofiles, visual objects, etc., embodiments of the invention are equallyapplicable to processing, organizing, storing and searching a wide rangeof content types including video, audio, text and signal files. Thus, anaudio embodiment may be used to provide a searchable database of andsearch audio files for speech, music, etc. Likewise, embodiments may bedirected to content in the form of or represented by text, signals, etc.

Embodiment of the invention include apparatus, software and method forproviding an online analysis of a particular online video resultreturned, for example by a textual search query, processing both thevisual video stream as well as its surrounding textual data, for thepurpose of offering the user a summarizing, interactive visualization,search and browsing experience for facilitating watching the video. Thisfeature introduces an interactive visual-relevance-rank ‘summary’,including thumbnails of the ‘relevant’ visual content (all most-visuallysalient hence relevant events, scenes and objects) as they are depictedin the video.

According to an embodiment of the invention, significant and apparentvisual elements within a retrieved set of textual search results aredetected using appropriate image object extraction engines andtechnology as described in Applicants' prior patent applicationsreferenced above (and incorporated herein by reference) and extractionengines produced by Videosurf, Inc. of San Mateo, Calif. See also P.Viola and M. Jones, “Rapid object detection using a boosted cascacd ofsimple features,” Proc. Computer Vision and Pattern Recognition, 2001.These video extraction engines technologies isolate, identify andprovide tracking of objects appearing in a video and, in particular,objects appearing in one or more frames or images of the video such as,faces, people, and other main/primary objects of interest. The extractedobjects are then scored for their relative visual significance withinthe overall video, ranking their relevance to the video content (i.e.obtaining their “visual relevance rank”). The rank of a visual metricmay be, for example, defined to be larger within the video to the extentthat the subset of all other prominent video images that are visuallysimilar to it is larger: e.g., a high visual-relevance-rank face is onewhich an image of the frame appears clearly and prominently many timeswithin the set of images in the video. A visual relevance-rank includes,for example, visual and context importance of a scene. For example, avisual importance may be based on visual characteristics of the scenethat a viewer might find important in determining whether the scene isinteresting or important to look at such as the presence of people,faces, objects followed by the camera, inclusion of a visually salientobject standing out from the background, duration of appearance of anobject, object size, contrast, position in the frame, etc. Contextualimportance may include, for example, characteristics, inclusion ofobjects or other features of the scene satisfying some externallyspecified criteria such as appearance of a person or other object beingsearched for within the scene.

Representative pictures or frames for the highest visual relevance ranksmay be automatically extracted and offered to the user as a thumbnail,‘click on’ feature, enabling a ‘jump to the depicted moment in thevideo’ experience. According to an embodiment of the invention, picturesidentified and extracted to represent the most significant visualelements in the video (highest visual relevance rank) may constitute athe filmstrip-like videos summary, as well as the chosen main videothumbnail.

Upon clicking on any picture in the summary the system may transitioninto a ‘video page’ on which it presents or screens to the user theparticular summarized video, starting from the particular momentdepicted by the particular filmstrip thumbnail clicked (which isgenerally an image taken form the video).

FIG. 1 is a flowchart of an embodiment of the invention for extractingrelevant scenes from a video and displaying a set static or dynamicthumbnails most representative of important scenes and/or features ofthe video. The thumbnails may include a main (or several primary)thumbnails that is (are) prominently displayed and is (are) consideredto be representative of the overall video, indicative of the presenceand/or importance of a target object that is identified by searchcriteria by which the video was selected and/or is (are) indicative of aclass or classes of object(s) found within the video (e.g., a particularperson or actor, faces, designated objects such as cars, etc.). Togetherwith the main thumbnail a filmstrip may be presented including anordered set or sequence of minor thumbnails. The thumbnails may beselected from respective segments or scenes appearing within the videothat are considered most important and may be particular frames selectedfrom each scene that are most representative of the scene, e.g., areimportant to the scene. Segment or scene importance may be based on somecriteria such as a significant appearance of an identified person withinthe segment or scene, a duration of an object (e.g., person) within thesegment or scene, position and size with respect to, for example, otherobjects including foreground and background objects and features, etc.Each of the thumbnails of the filmstrip may be selected from frames ofthe video corresponding to selected scenes of the video. Again, minorthumbnail selection may be optimized to emphasize some important featureof or object appearing in the corresponding scene such as an objectcorresponding to some search criteria as in the case of the mainthumbnail.

Visual Summary of a Video

To display search results representative frames are extracted out of agiven video and may be aligned in a row in such a way that each row ofimages represents the video in the best way to the end use. The variousframes of a video identified as a candidate for selection by a userinclude display of frames for the video based both on their importancein the video and on their difference from each other, making a blendthat ends up in showing a variety of the different most important visualmoments in the video (see FIG. 3, parts 341 and 343). The overallmethodology can be divided into three major component processes or, inthe case of an apparatus, engine or processor for performing thefollowing, as will be explained in more detail with reference to FIG. 1of the drawings:

1. Extracting the frames.

2. Scoring the frames.

3. Deciding which frames to show.

Extracting frames from the video includes shot detection and extractionof representative frames as described in the following sections.

Shot Detection

With reference to step 101 of FIG. 1, detecting or identifying scenes(also referred to as“shots”) or a series of related frames associatedwith a scene within the video typically is accomplished as an initialstep. A shot is determined by a sharp transition in the video's framescharacteristics. One significant characteristic used may be the colorhistogram of the underlying image.

For each pair of consecutive frames in the video, the ‘distance’ betweenframes is measured in terms of the Bhattacharyya coefficient between thetwo underlying color histograms of the images. See Bhattacharyya, A.(1943). “On a measure of divergence between two statistical populationsdefined by their probability distributions” and U.S. Patent PublicationNo. 2008/0193017 entitled “Method for detecting scene boundaries ingenre independent videos.” Bulletin of the Calcutta Mathematical Society35: 99-109. MR0010358. These color histograms consist of 64equi-partitioned bins in the RGB space of the underlying image. Othercharacteristics of the images can be taken into account, such as themotion between the two images, the objects detected in them and more.

After having the distance d(t) between frame number t and frame numbert+1 in the video (for all t values between 1 and the number offrames-1), points in the graph of the function d(t) differ by much fromtheir neighborhood are identified. In more detail, the point t₀ isconsidered as a scene or shot transition whenever d(t₀) is larger by,for example, at least three and, more preferable, at least five standarddeviations from a certain version of the moving average of d(t) and isalso larger by at least some pre-defined threshold from the same movingaverage. Also, to avoid random fluctuations being detected as shottransitions, the immediate neighborhood of the candidate t₀ is checkedto confirm that it is not also detected as a scene or shot transition.

Representative Frames Extraction

Next, at step 102, representative frames may be extracted from eachscene or shot by clustering the frames in the shot to ‘sub-shots’, usinga 1-dimensional version of a general clustering method, based on themulti-grid methodology. See also On Spectral Clustering: Analysis and analgorithm (2001) by Andrew Y. Ng, Michael I. Jordan, Yair Weisshttp://citeseerx.ist.psu.edu/viewdoc/summag?doi=10.1.1.19.8100. Asub-shot is a contiguous set of frames within the shot that has commoncharacteristics—in particular, the frames within any given sub-shotusually come from the same scene in the video. This algorithm alsoprovides a representative frame for each sub-shot—the frame thatrepresents it the most and would be the most suitable to show to theuser as the thumbnail of this ‘sub-shot’.

A preferred clustering algorithm used is a bottom-up algorithm. It worksin O(log(the number of frames)) iterations. The input to the firstiteration is the set of all frames in the shot, along with theirdistances from their two direct neighbors (the neighbors of a frame arethe two frames that are displayed right before and right after it). Inthe first iteration, a subset C=C(0) of the frames is chosen such thatevery frame is either in C or a neighbor of a frame in C. The distanceof any given frame to a member of C is also checked to ensure that it isnot too large (compared to a threshold). Then, the output of the firstiteration is the set C, along with new distances between neighboringmembers of the C-set (neighbors here correspond to frames that have onlynon-C members along the path between them). The set C is the input tothe second iteration, which in turn produces a new set C′=C(1) (which isa subset of the set C). The process concludes when the resulting setC(n) contains only frames that are sufficiently distant from theirneighbors. Since the size of C(n) is smaller by constant factor thanC(n−1), the algorithm is considered to be very fast.

Tubes Extraction

“Tubes” are next extracted at step 103, each tube containing detectedand tracked objects within each shot. See K. Okuma, A. Teleghani, N. deFreitas, J. Little and D. Lowe. A boosted particle filter: Multi-targetdetection and tracking, ECCV, 2004http://www.springerlink.com/content/wvflnw3xw53xjnf3/for other commonmethods used to track objects. A tube is a contiguous set of ROI's(regions of interest) within a video's frames, containing a trackedobject (e.g. a face)—defining a spatio-temporal region within the videothat contains the tracked object. Along with a tube its representativeROI is also defined which then defines its frames. The tube'srepresentative frames are the frames in which the object appears in itsmost prominent way, and in a way that reflects the variety ofappearances of the object in the tube.

The extraction of tubes may be performed as follows. Assuming theavailability of a still image based detector for a given object type(e.g. a still image face detector etc.), the detector is applied tospecial frames of the video, called ‘key-frames’. The key-frames arechosen so that every shot will have a key-frame in the beginning of theshot, and the maximum distance between consecutive key-frames will notbe larger than a pre-defined number of frames (typically around 40frames).

Whenever an object is detected in a key-frame, tracking of the object isinitiated along the video's frames, until arriving at the next key-frame(or until the end of the shot or scene). Tracking is performed, forexample, either by applying the detector once again in the neighborhoodof the expected region of interest or by other means of tracking(optical-flow based tracking, mean shift, CAM-shift and more). When thetracking process arrives at a new key-frame, the detector is appliedonce again on the whole image and the ‘live tubes’ (those tubes thatbegan in a previous key-frame and were tracked to the current point inthe video) are added to the detected object in the appropriate ROI's.The tracking method is based on an Hidden Markov Model (HMM) approach(See for example Lawrence R. Rabiner (February 1989). “A tutorial onHidden Markov Models and selected applications in speech recognition”.It is integrating the temporal signal available in video in order toincrease the accuracy and the robustness of the method to occlusions,and noise and other factors that usually impede detection that is doneon each frame independently. This integration is computed using adynamic programming method that is most efficient and suitable for HMM(See Aji, S. M. McEliece, R. J. The generalized distributive law:http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F18%2F17872%2F00825794.pdf%3Farnumber%3D825794&authDecision=−203for further discussion.)

Another kind of tubes is a ‘motion tube’, where an object is detected byits motion (in contrast to the background). In these cases the fact thatan object appears in the tube is detected, although the kind ofunderlying object may not be detected or identified.

Identifying and Grouping Together Similar Tubes

Identifying large groups of tubes that represent the same object may beimportant for generating an accurate summary and search relevancy.However, tubes of an object generally appear at different,non-contiguous times with different visual appearances (e.g. a personmight look forwards at one scene and then sideways at another).Therefore, tube grouping may be performed by a clustering algorithm thatuses a matrix of pairwise similarities between all tubes. Pair-wisesimilarities may consist of integrating many similarities betweencorresponding tube parts. This integration of similarities as well asintegrating temporal information addresses high object variability: whencomparing the parts of two tubes all pairs of corresponding parts fromall instances may be compared to provide a good overall match despite afew partial mismatches.

Every part is represented by an image region separated from thebackground. The direct similarity between corresponding regions (parts)may be measured using one or more of several known methods including butnot limited to L2 distance between the regions as well as the L2distance between features extracted from these regions (e.g. horizontal,diagonal and vertical edges, shift invariance feature transforms (SIFT),see Lowe, David G. (1999). “Object recognition from localscale-invariant features”. Proceedings of the International Conferenceon Computer Vision 2: 1150-1157. doi:10.1109/ICCV.1999.790410 or otherlocal features: Mikolajczyk, K.; Schmid, C. (2005). “A performanceevaluation of local descriptors”. IEEE Transactions on Pattern Analysisand Machine Intelligence 27: 1615-1630.).

The direct pairwise similarities are expensive to compute though andcomputation therefore proceeds in a progressive coarse to fine manner.Each part is assigned with a “signature” that represents its type (e.g.open vs. close mouth). The signatures are determined by comparing eachinstance part with a large bank of optional signatures. This bank islearned a-priori from many examples and a part signature is determinedby matching it with the best one available in the bank. Pairs ofinstances having dissimilar signatures are filtered out from furthercomparisons (e.g. closed and an open eye regions are generally notcompared). Parts of pairs of tubes that share similar signatures arefurther compared by the direct method to generate the tubes-similaritymatrix for the clustering algorithm. The clustering algorithm thenidentifies groups of tubes likely to represent a single object. Thealgorithm is very similar to the bottom-up clustering algorithmmentioned above at the “representative frame extraction” part. Eachgroup is also identified with a representative tube and a representativeframe. The grouping process also enables better evaluation of the tubeimportance in the videos: the bigger a tube's group is, the moreimportant it becomes.

Scoring the Frames

Each representative frame (from both tubes and sub-shots) is given animportance score at step 104, based on its characteristics. Arepresentative frame of a tube is given a higher score, based on thelength of the tube in the video and on the detected object (e.g. a framecontaining a face gets a higher score than a ‘non-face’ frame). Atext-frame is given a low score (e.g credits at the end of the video).Other features of the frame that affect the score are the‘color-distinctiveness’ of the frame (how spread is the distribution ofcolors in the underlying image), the ‘centrality’ of the frame (how muchis the frame likely to contain an undetected central object—usingaggregated color blobs in the image) and other features.

Deciding Which Frames to Show

Each representative frame is assigned a ‘display-order’ at step 105. Thedisplay order is a serial or sequence number assigned to eachrepresentative frame (from one to the number of representative frames),that determines which frames will be shown. In cases where a singleframe is to be shown, that is assigned with display order 1 (the ‘mainthumbnail’); In cases where ten frames are to be shown (or any othernumber k), the frames are were assigned with display order 1 to 10 (1 tok, respectively), ordered chronologically by their appearance in thevideo. See FIG. 3, main thumbnail 341 and visual summary of the video343.

In order to determine which frames to show the distance between any tworepresentative frames is measured. This distance is based on theunderlying detected object (whenever it exists) and general features ofthe underlying image, such as the distribution of colors in the frame.

The frame with display order 1 is the frame having the maximum score ofall representative frames. The frame with display order 2 is the framethat maximizes a weighted sum of its score and the distance measure fromthe main thumbnail so that a substantially different image than thefirst one is included while visually similar frames may be excluded fromthe selection process. After the frames with display order 1,2, . . . ,k have been determined, the k+1'th frame is determined by maximizing theweighted sum of its score and the distance from frames 1,2, . . . , k(the maximum is taken among the frames not included in the first kframes). Ordering is completed when all of the representative frameshave been exhausted. In case that less than a small pre-defined numberof representative frames are extracted, further augmentations of theframes is performed by sampling more frames from the video. For example,this is the case where the video is just a video containing a singlescene. Finally, at step 106, the representative frames of the video areavailable for display to the user on their computer or other terminalrunning the appropriate software (e.g., web browser.). The result isused both on the search results page (see, e.g. FIGS. 3 and 4) and onthe video page (see, e.g., FIG. 5).

Visual relevance ranking can be used in various applications to providean enhanced display of video of interest to a user. One such applicationincludes an interactive web searching application for browsing videocontent on the Internet and World Wide Web (WWW.) By combining visualrelevance rank ranking with an interactive video search engine, a usercan “see” inside videos to find content in a fast, efficient, andscalable way. Basing content search on visual identification, ratherthan text only, a vision-based video search engine provides morerelevant results and a better experience to let users find and discoverthe videos they really want to watch.

2. A Visual-Relevance-Rank Feedback and Subsequent Search

Employing the described visual relevance ranking in a visual searchengine provides an online capability to analyze a set of video resultsreturned by a textual search query, processing both the visual streamsas well as their surrounding textual data for the purpose of offeringthe user a subsequent, interactive visual search experience. Thisfeature provides an enhanced interactive visual-relevance-rankimprovement of the set of search results served first.

According to an embodiment of the invention, significant and apparentvisual elements within a retrieved set of textual search results aredetected using appropriate image object extraction technology such asprovided by Videosurf's Computer Vision propriety extractiontechnologies (e.g. faces, people, and main objects of interest). See,e.g., P. Viola, M. Jones “Rapid object detection using a boosted cascadeof simple features”, Proc. CVPR, Vol. 1 (2001), pp. 511-518. Theextracted objected are then scored for their relative visualsignificance within the entire set of results, ranking this way theirrelevance to the original textual query submitted by the user (i.e.obtaining their ‘visual relevance rank rank’). The rank of a visualentity is defined to be larger within the entire set of retrieved searchresults to the extent that the subset of other visual entities similarto it is larger; e.g., a high visual-relevance-rank face is one whichappears many times within the set of search results (e.g. searching for‘Britney’ videos will presumably reproduce many ‘Britney’ faces, andother faces associated with Britney).

Representative pictures for the highest visual relevance ranks areautomatically extracted and offered to the user for a ‘click on’feature, enabling a higher visual-relevance-rank subsequent searchexperience (that is, a ‘see more of’, and/or ‘see more like’experience). According to an embodiment of the invention, picturesidentified and extracted to represent the most significant visualelements in the results (highest visual relevance rank rank) are posted,for instance, on a north/top, face-strip bar with a suggestive ‘see moreof’ functionality above the customary main-page offering of the textualsearch results (see also FIG. 1). The user may then click on any subsetof the face-strip pictures so as to increase the relevance within andreordering the entire bulk of returned search results, to surface up andpromote the content/faces depicted in those pictures clicked; henceexecuting a higher visual-relevance-rank as a subsequent, interactivesearch.

Upon clicking on any picture in the face strip the system makes use ofall the available text associated with the video from which this pictureis coming (e.g., metadata, text identified in proximity to the videosuch as a title of the video), all possible tags which may have beenassociated with the picture itself using any user and/or editorialinteraction, and any other available information exposed by way of anyvisual similarity of this picture, and the elements depicted in it toany other visual content available. The picture clicked may be one of aperson to which none or insufficient tagging/text was ever associatedanywhere online, but to which other pictures available elsewhere onlinemay be similar enough to suggest that the two pictures are of the sameperson. Such visual similarity is then exploited to link between allpieces of information, grouping them together and including anyresulting newly discovered information in the results of the subsequentsearch for higher visual-relevance-rank results.

To further explain the functionality of the pictures presented in thetop-left of an embodiment of a user interface screen, ‘refine results byperson’. If these pictures already have tags associated to them thesetags will appear in the blue links 322 beneath such picture (see, e.g.,blue text located beneath respective thumbnails 321 of visual searcharea 320 in FIG. 3.) In case a picture has no tag associated to it yet,the same blue link will offer an on-the-spot ‘tag me’ feature. Upontagging any such picture, grouping of faces (or other objects) based onvisual-similarity may be implemented in order to diffuse and apply thesame tag to other appearances of the same face within the face strip. Asuitable engine for determining visual similarity is provided byVideosurf, Inc. Moreover, in a similar manner, automatic tagging will beemployed to any of the faces in the face-strip by way of associatingthem with other tagged face appearances which may reside elsewhere inthe database, or anywhere else online for that matter.

The referenced engine available from by Videosurf includes its Videosurfproduct implementing video technology using computer science, machinelearning, mathematics and computer vision technologies, combined withany human computation and editorial resources to efficiently analyze allavailable content for the pictures of high significance and for exposingthe visual similarities between all such pictures and the elementsappearing in them. The technology may use motion and visual features, aswell as visual machine recognition to concentrate on the objects ofinterest in each of the pictures. The methods are efficient and scalableand can cover large, comprehensive bulks of textual and visual content.

The invention supports embodiments incorporating technologies fordetecting the elements of high visual significance in a video, or withina collection of videos. Embodiments and applications include methods andapparatus that provide the following:

1. The contiguous sequences of appearances of each face in a video aredetected and each generates a ‘face tube’. For each such face-tube theduration of appearance (screen time) is persisted, as well as the facesizes, frontal-views duration/screen-time, etc.

2. Similarly to face-tubes, the more general object ‘motion-tubes’ areextracted; for detecting and extracting any object which appears to bedistinguished and focused on in the video, contiguously through itsmotion, in all different such instances.

3. Interesting and outstanding motion events and actions are recorded.

4. Interesting and salient visual appearances are recorded.

5. Representative coverage of the overall visual appearances in thevideo(s) recorded.

Each such significant appearance of any visual element was originallyextracted from a specific video to which textual metadata was associatedin the initial uploading to the hosting/streaming site (e.g. title,tags, description). This associated text may be used as well as thevisual similarity between these extracted elements in order to groupthem together and serve only different enough visual elements for the‘refine results by person’ feature of the higher visual-relevance-ranksubsequent search.

According to an embodiment of the invention, a visual search system mayinclude the following steps/processes:

1. A list of popular and common Search Queries may be dynamically ismaintained:

-   -   a. The list may be produced editorially, using for example,        lists of celebrities, or by mTurk        (https://www.mturk.com/mturk/welcome) hired internet users and        workers and/or other external sources; and    -   b. The system registers the real popular search queries and        keeps track

2. For each query in a Search Query List a corresponding resulting setof videos is produced to provide a QV Set (Query-led Video Set).

-   -   a. Each QV Set may be pre-processed to extract some or all of        its important face-tube groups        -   i Each face-tube group represents one interesting face    -   b. Each video may belong to several QV Sets.

3. Groups of similar face-tube within the QV Sets are being formed priorand alongside to their visual similarity through the ‘TaggedAppearances’ (human or automatic tagging of some face tubes).

-   -   a. Seeded at the tagged-appearances ground truth a tube-2-tube        visual similarity/recognition score between all tubes may be        followed by a grouping mechanism to generate the major face-tube        groups (pair-wise scores plus clustering.)

4. For a search already listed in our Search Query List, a group ofrepresentative pictures for Visual Search may be prepared offline,similar to the following online operation

5. Given a new search performed in real time, if it does not alreadybelong to the Search Query List, its list of high-relevance videos V1, .. . VN is processed, each of which belongs at most to only few QV Setsby design.

-   -   a. For each of these few QV Set a few main face-tube groups are        obtained, intersected with V1, . . . VN    -   b. For each such face-tube group a representative picture for        Visual Search is brought up.    -   c. Once clicked each such picture is brought up to the top of        the results of all videos for which the corresponding face-tube        group is major component.        -   i. Tags associated with the picture chosen from the visual            search should enhance the scoring of videos carrying those            tags.

6. Freshness: for a new video we *‘temporarily assign’* the face-tubegroups/people it belongs to by data/text similarity to videos in thesegroups. Later on face-tube visual indexing should become immediate anddone as soon as a new video, especially from popular and high qualitysources, arrives.

7. Note: the searches performed, and all clicks may be registered forimmediate reinforcement and future use.

FIG. 2 is a flowchart of an embodiment of the invention. At 201 a usermay input a search string or expression. A search for appropriatecontent, such as videos, photos, etc., is performed at 202 based on thesearch terms or query. The search may be conducted over and/or usingseveral databases and facilities such as the Internet. The searchresults are ranked at 203 so that the most relevant are identified. At204 image extraction identifies significant visual object present in thesearch results. For example, objects identified as a person presentwithin the video may be identified and extracted, with similar objectsidentified and tracked through each video at 205. At 206 a ranking maybe performed to identify a significance of the tracked object within thevideo. Criteria may include the relative size of the visual object,percentage of time that the object is present in the video, etc. A videothumbnail representing the extracted visual object is generated at 207and associated with the respective intra-video visual object tube. Step208 provides for labeling or naming the visual object. This may be doneautomatically by the use of associated metadata, a search for similarobject that have been previously identified and labeled, or the use ofother recognition techniques. Alternatively, a user may be prompted toinput a label for the visual object. Alternatively, identification andlabeling of the visual object may be performed after visual object aregrouped to identify visual objects present in multiple videos therebyforming inter-video visual object tubes.

At 209 visual object common to multiple videos form the basis ofgrouping those videos to form a visual object tube. Step 210 selectsand/or generates a suitable thumbnail representative of the visualobject tube, e.g., depicting a person or object appearing throughout thegroup of videos. These thumbnails may then be displayed across, forexample, a top portion of the display, e.g., inter-video visual objectsthumbnails 102 as shown in FIG. 1. The groups also may be ranked forrelevancy to the original search query by fusing a quality of matchmetric resulting from the textual search with a weighting from thevisual object extraction process. This overall ranking may be used at212 to display an ordered listing of search results at 213 (see searchresults 105 of FIG. 1).

At 214 A user may then select a group thumbnail 103 of interest and/orlabel one of more thumbnail 104 at step 215. Upon selecting a visualobject of interest, the search results display 105 may be reordered at216 to show videos including the selected video object, again rankedbased on one or more criteria. The visual object listing 102 may besimilarly reordered based on the selection.

FIG. 3 is a screen shot or screen capture of a user interface screenaccording to an embodiment of the invention. The interface may includean advanced-options bar 310 including a text-bar input area 312 allowinga user to input a search string or expression describing targetedobjects, such as videos, to be located and identified. Objects, e.g.,videos, satisfying the search criteria may be listed and displayed inorder of relevance. For example, those videos matching the search stringin the title of the video may be considered more significant or a bettermatch than videos wherein the search string is only found in metadataassociated with the video. Other criteria may include popularity,frequency, number and recency of accesses/references to a video, qualityand length. Each matching video may be represented by a characteristicof main thumbnail 342 and series of thumbnail images 344 from the videoin the form of a filmstrip also called a “video summary.”.Characteristic thumbnail 342 may be obtained by extracting a mostsignificant visual object identified in each video to define intra-videovisual object tubes. Grouping of the most significant visual objectsfound amongst the search results may be performed to identifysignificant objects to define inter-video visual object tubes. Theinter-video object tubes may be ranked and displayed as represented byappropriate spotlight-like thumbnails 321 depicting an object (e.g.,face) that forms the basis of the inter-video object tube (e.g., animage of a person that is included in and/or the subject of the videoswithin and forming a group of videos). Such highest-rank inter-videoobjects (see 321) are suggested to the user in the “Refine Results byPerson’ feature to click on, leading to a subsequent search as depictedin FIG. 4.

An example of a text search followed by a video search is depicted inFIGS. 3 and 4. FIG. 3 is a screen shot displaying the search results forthe text query ‘lost’ and the most relevant videos it retrieves. Asshown, the four most relevant search results are displayed, togetherwith snapshot or clips from the associated video while many more videoswith lower relevancy are available by scrolling down using theright-hand scroll bar. FIG. 4 is a screen shot showing the results of asubsequent image or video search, displaying results after a user clickson, for example, the top-second-left celebrity in the Visual-Searchtop-left ‘Refine Results by Person’ feature 420 (e.g., a celebrity suchas Evangeline Lilly; see 422 denoted also by being the only image inthis feature remaining highlighted). According to this example and anembodiment of the invention, the order of the top-relevant videoschanges with respect the presence of the person (in this example,celebrity Evangeline Lilly) in these top videos, as well as thethumbnails and the summary clips that are likewise changed to representthe celebrity Evangeline Lilly intra-vids groups. FIG. 5 is a screenshotresulting from selection of a video to be played, the selected videoretrieved (if required) and then played by a suitable media or videoplayer in a major portion of the screen. The thumbnails constituting theassociated filmstrip depicting scenes detected within the video may bedisplayed, for example, along a bottom of the screen. A window alonganother portion of the screen (in this case, along the right of thescreen) may provide textual information about the video or clip to be orbeing played, provide for selection of related or suggested videos, andprovide other functionalities.

FIG. 6 is a block diagram of a computer platform for executing computerprogram code implementing processes and steps according to variousembodiments of the invention. Object processing and database searchingmay be performed by computer system 600 in which central processing unit(CPU) 601 is coupled to system bus 602. CPU 601 may be any generalpurpose CPU. The present invention is not restricted by the architectureof CPU 601 (or other components of exemplary system 600) as long as CPU601 (and other components of system 600) supports the inventiveoperations as described herein. CPU 601 may execute the various logicalinstructions according to embodiments of the present invention. Forexample, CPU 601 may execute machine-level instructions according to theexemplary operational flows described above in conjunction with FIGS. 1and 2.

Computer system 600 also preferably includes random access memory (RAM)603, which may be SRAM, DRAM, SDRAM, or the like. Computer system 600preferably includes read-only memory (ROM) 604 which may be PROM, EPROM,EEPROM, or the like. RAM 603 and ROM 604 hold/store user and system dataand programs, such as a machine-readable and/or executable program ofinstructions for object extraction and/or video indexing according toembodiments of the present invention.

Computer system 600 also preferably includes input/output (I/O) adapter605, communications adapter 611, user interface adapter 608, and displayadapter 609. I/O adapter 605, user interface adapter 608, and/orcommunications adapter 611 may, in certain embodiments, enable a user tointeract with computer system 600 in order to input information.

I/O adapter 605 preferably connects to storage device(s) 606, such asone or more of hard drive, compact disc (CD) drive, floppy disk drive,tape drive, etc. to computer system 600. The storage devices may beutilized when RAM 603 is insufficient for the memory requirementsassociated with storing data for operations of the system (e.g., storageof videos and related information). Although RAM 603, ROM 604 and/orstorage device(s) 606 may include media suitable for storing a programof instructions for video process, object extraction and/or videoindexing according to embodiments of the present invention, those havingremovable media may also be used to load the program and/or bulk datasuch as large video files.

Communications adapter 611 is preferably adapted to couple computersystem 600 to network 612, which may enable information to be input toand/or output from system 600 via such network 612 (e.g., the Internetor other wide-area network, a local-area network, a public or privateswitched telephony network, a wireless network, any combination of theforegoing). For instance, users identifying or otherwise supplying avideo for processing may remotely input access information or videofiles to system 600 via network 612 from a remote computer. Userinterface adapter 608 couples user input devices, such as keyboard 613,pointing device 607, and microphone 614 and/or output devices, such asspeaker(s) 615 to computer system 600. Display adapter 609 is driven byCPU 601 to control the display on display device 610 to, for example,display information regarding a video being processed and providing forinteraction of a local user or system operator during object extractionand/or video indexing operations.

It shall be appreciated that the present invention and embodimentsthereof are not limited to the architecture of system 600. For example,any suitable processor-based device may be utilized for implementingobject extraction and video indexing, including without limitationpersonal computers, laptop computers, computer workstations, andmulti-processor servers. Moreover, embodiments of the present inventionmay be implemented on application specific integrated circuits (ASICs)or very large scale integrated (VLSI) circuits. In fact, persons ofordinary skill in the art may utilize any number of suitable structurescapable of executing logical operations according to the embodiments ofthe present invention.

While the foregoing has described what are considered to be the bestmode and/or other preferred embodiments of the invention, it isunderstood that various modifications may be made therein and that theinvention may be implemented in various forms and embodiments, and thatit may be applied in numerous applications, only some of which have beendescribed herein. It is intended by the following claims to claim anyand all modifications and variations that fall within the true scope ofthe inventive concepts.

It should also be noted and understood that all publications, patentsand patent applications mentioned in this specification are indicativeof the level of skill in the art to which the invention pertains. Allpublications, patents and patent applications are herein incorporated byreference to the same extent as if each individual publication, patentor patent application was specifically and individually indicated to beincorporated by reference in its entirety.

1. A method of identifying a video comprising the steps of: identifyingsequences of frames of the video as comprising respective scenes;determining visual relevance rank of each of the scenes; selecting anumber of the scenes based on the visual relevance rank associated witheach of the scenes; identifying, within each of the selected scenes, arepresentative thumbnail frame; and displaying (i) a first thumbnailcorresponding to one of the representative thumbnail frames based on thevisual relevance rank of the associated scene and (ii) a filmstripincluding an ordered sequence of the representative thumbnail frames. 2.The method according to claim 1 further comprising linking each of thethumbnails to a corresponding one of the scenes.
 3. The method accordingto claim 1 further comprising recognizing a selection of one of thethumbnails and playing the video starting at the scene corresponding tothe selected thumbnail.
 4. The method according to claim 1 wherein thevisual relevance rank is based on a visual importance of the associatedscene.
 5. The method according to claim 1 wherein the visual relevancerank is based on a contextual importance of the associated scene.
 6. Themethod according to claim 1 wherein the step of identifying includesdesignating a type of object to be included in each of therepresentative thumbnail frames.
 7. The method according to claim 1wherein the type of object is selected from the group consisting offaces, people, cares and moving objects.
 8. The method according toclaim 1 wherein the visual relevance rank of a scene is downgraded forthose scenes having specified characteristics.
 9. The method accordingto claim 8 wherein the specified characteristics are selected from thegroup consisting of scenes having low contrast images, scenes having asignificant textual content and scenes having relatively little or nomotion.
 10. The method according to claim 1 wherein the step ofidentifying sequences of frames of the video as comprising respectivescenes includes identifying one or more regions of interest appearing inthe frames and segmenting sequences of the frames into scenes based oncontinuity of objects appearing in frames of the sequences of frames.11. The method according to claim 1 wherein the step of identifyingsequences of frames of the video as comprising respective scenesincludes identifying objects appearing in the frames and segmentingsequences of the frames into scenes based on continuity of objectsappearing in frames of the sequences of frames.
 12. The method accordingto claim 1 wherein the step of identifying sequences of frames of thevideo as comprising respective scenes includes identifying an objectappearing in the frames and segmenting sequences of the frames intoscenes based the object appearing in frames of the sequences of frames.13. The method according to claim 12 wherein the frames of the sequenceof frames are discontiguous.
 14. The method according to claim 1 whereinthe step of determining visual relevance rank of each of the scenes isdetermined by identifying an object appearing in the scene thatcorrespond to a target object specified by search criteria.
 15. Themethod according to claim 14 wherein the target object is a person. 16.The method according to claim 1 wherein the step of determining thevisual relevance rank of each of the scenes includes identifying a tubelength corresponding to a duration of each of the scenes and, inresponse, calculating a visual relevance rank score for each of thescenes.
 17. The method according to claim 1 wherein the step ofidentifying, within each of the selected scenes, a representativethumbnail frame includes reviewing frames of each of the selected scenesand selecting a frame as a representative thumbnail including an objectvisual relevance rank to an associated scene.
 18. The method accordingto claim 1 wherein the step of selecting a frame as a representativethumbnail includes identifying an object appearing in a scene thatcorresponds to a target object specified by search criteria.
 19. Themethod according to claim 1 wherein the step of displaying a filmstripincludes ordering the representative thumbnail frames in a temporalsequence corresponding to an order of appearance of the selected scenesin the video.
 20. The method according to claim 1 further comprising thesteps of: identifying a selection of a thumbnail associated with thevideo; and playing the video.